Robotic ironing with 3D perception and force/torque feedback in household environments

As robotic systems become more popular in household environments, the complexity of required tasks also increases. In this work we focus on a domestic chore deemed dull by a majority of the population, the task of ironing. The presented algorithm improves on the limited number of previous works by joining 3D perception with force/torque sensing, with emphasis on finding a practical solution with a feasible implementation in a domestic setting. Our algorithm obtains a point cloud representation of the working environment. From this point cloud, the garment is segmented and a custom Wrinkleness Local Descriptor (WiLD) is computed to determine the location of the present wrinkles. Using this descriptor, the most suitable ironing path is computed and, based on it, the manipulation algorithm performs the force-controlled ironing operation. Experiments have been performed with a humanoid robot platform, proving that our algorithm is able to detect successfully wrinkles present in garments and iteratively reduce the wrinkleness using an unmodified iron.

[1]  Nico Blodow,et al.  Combined 2D–3D categorization and classification for multimodal perception systems , 2011, Int. J. Robotics Res..

[2]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[3]  Yan Bai,et al.  Review of cloth modeling , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

[4]  Dejan Pangercic,et al.  Robotic roommates making pancakes , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[5]  Carlos Balaguer,et al.  Personal Autonomy Rehabilitation in Home Environments by a Portable Assistive Robot , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Danfei Xu,et al.  Multi-sensor surface analysis for robotic ironing , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[8]  Darwin G. Caldwell,et al.  Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input , 2011, Adv. Robotics.

[9]  Rüdiger Dillmann,et al.  ARMAR-III: Advances in Humanoid Grasping and Manipulation , 2013 .

[10]  Sören Kammel,et al.  Bimanual robotic cloth manipulation for laundry folding , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Jian S. Dai,et al.  Trajectory and orientation analysis of the ironing process for robotic automation , 2004 .

[12]  Jian S. Dai,et al.  Folding algorithms and mechanisms synthesis for robotic ironing , 2004 .

[13]  Carlos Balaguer,et al.  TEO: FULL-SIZE HUMANOID ROBOT DESIGN POWERED BY A FUEL CELL SYSTEM , 2012, Cybern. Syst..